1,721,000 research outputs found
Towards a Statistical Approach to Neural Networks: an Integrated Strategy in a New Software Tool
No full textLa regressione quantile per la valutazione dell'efficacia dei processi formativi universitari
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Modelling students’ performance in MOOCs: a multivariate approach
No full textMassive Open Online Courses, universally labelled as MOOCs, become more and more relevant in the era of digitalization of higher education. The availability of free education resources without access restrictions for a plenty of potential users has changed the learning market in a way unthinkable only few decades ago. This form of web-based education allows to track all the actions of the students, thus providing an information base to understand how students' behaviour can influence their performance. The paper proposes a structural equation model in the framework of the component-based approach to measure which are the main factors affecting students' performance (Partial Least Squares Path Modelling). The novelty of the approach is the simultaneous analysis of more than one factor that exerts an impact on the performance. The analysis is carried out on the log data of a course available on the edX MOOCs platform named FedericaX
Handling multicollinearity in quantile regression through the use of principal component regression
Full text linkIn many fields of applications, linear regression is the most widely used statistical method to analyze the effect of a set of explanatory variables on a response variable of interest. Classical least squares regression focuses on the conditional mean of the response, while quantile regression extends the view to conditional quantiles. Quantile regression is very convenient, whereas classical parametric assumptions do not hold and/or when relevant information lies in the tails and therefore the interest is in modeling the conditional distribution of the response at locations different from the mean. A situation common to most regression applications is the presence of strong correlations between predictors. This leads to the well-known problem of collinearity. While the effects of collinearity on least squares estimates are well investigated, this is not the case for quantile regression estimates. This paper aims to explore the collinearity problem in quantile regression. First, a simulation study analyses the problem concerning different degrees of collinearity and various response distributions. Then the paper proposes using regression on latent components as a possible solution to collinearity in quantile regression. Finally, a case study shows the assessment of the quality of service in the presence of highly correlated predictors
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